Title

Artificial neural networks and logit models for traffic safety analysis of toll plazas

Authors

Authors

H. T. Abdelwahab; M. A. Abdel-Aty;Trb

Comments

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Keywords

ACCIDENT SEVERITY; Engineering, Civil; Statistics & Probability; Transportation; Transportation Science & Technology

Abstract

Little research has been conducted to evaluate the traffic safety of toll plazas and the impact of electronic toll collection (ETC) systems on highway safety, but analyses indicate that toll plazas do contribute to traffic accidents. Traffic safety issues related to toll plazas and ETC systems were studied using the 1999 and 2000 toll plaza traffic accident reports of the Central Florida expressway system. The analysis focused on accident location with respect to the plaza structure (before, at, after plaza) and driver injury severity (no injury, possible, evident, severe injuries). Two well-known artificial neural network (ANN) paradigms were investigated: the Multi-Layer Perceptron and Radial Basis Functions neural networks. The performance of ANN was compared with calibrated logit models. Modeling results showed that vehicles equipped with ETC devices, especially medium/heavy-duty trucks, have higher risk of being involved in accidents at the toll plaza structure. Also, main-line toll plazas have a higher percentage of accident occurrence upstream of the toll plaza. In terms of driver injury severity, ETC users have a higher chance of being injured when involved in an accident. Older drivers tend to have higher risk of experiencing more severe injuries than younger drivers. Female drivers have a higher chance of experiencing a severe injury than do mate drivers.

Journal Title

Statistical Methodology: Applications to Design, Data Analysis, and Evaluation: Safety and Human Performance

Issue/Number

1784

Publication Date

1-1-2002

Document Type

Article

Language

English

First Page

115

Last Page

125

WOS Identifier

WOS:000179975400015

ISSN

0361-1981; 0-309-07708-7

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